class_docstr: Hand-coded policy that returns random actions.
class_docstr_tok: ["Hand-coded","policy","that","returns","random","actions","."]
docstr: No learning.
docstr_tok: ["No","learning","."]
code: def learn_on_batch(self, samples):
      """No learning."""
      return {}
  
file_path: ray/python/ray/rllib/examples/policy/random_policy.py
imports: from gymnasium.spaces import Box
  import numpy
  import random
  import tree
  from typing import List, Optional, Union
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import ModelWeights, TensorStructType, TensorType
label_desc: Use image node_id 4 for calling the RandomPolicy obj's underlying member method code with example usage: obj.learn_on_batch(samples) and returns: dict